License_plate_recognition/test_lpr_real_images.py
2025-09-04 00:10:18 +08:00

99 lines
3.1 KiB
Python

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
LPRNet接口真实图片测试脚本
测试LPRNET_part目录下的真实车牌图片
"""
import cv2
import numpy as np
import os
from LPRNET_part.lpr_interface import LPRNinitialize_model, LPRNmodel_predict
def test_real_images():
"""
测试LPRNET_part目录下的真实车牌图片
"""
print("=== LPRNet真实图片测试 ===")
# 初始化模型
print("1. 初始化LPRNet模型...")
success = LPRNinitialize_model()
if not success:
print("模型初始化失败!")
return
# 获取LPRNET_part目录下的图片文件
lprnet_dir = "LPRNET_part"
image_files = []
if os.path.exists(lprnet_dir):
for file in os.listdir(lprnet_dir):
if file.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp')):
image_files.append(os.path.join(lprnet_dir, file))
if not image_files:
print("未找到图片文件!")
return
print(f"2. 找到 {len(image_files)} 个图片文件")
# 测试每个图片
for i, image_path in enumerate(image_files, 1):
print(f"\n--- 测试图片 {i}: {os.path.basename(image_path)} ---")
try:
# 使用支持中文路径的方式读取图片
image = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_COLOR)
if image is None:
print(f"无法读取图片: {image_path}")
continue
print(f"图片尺寸: {image.shape}")
# 进行预测
result = LPRNmodel_predict(image)
print(f"识别结果: {result}")
print(f"识别车牌号: {''.join(result)}")
except Exception as e:
print(f"处理图片 {image_path} 时出错: {e}")
import traceback
traceback.print_exc()
print("\n=== 测试完成 ===")
def test_image_loading():
"""
测试图片加载方式
"""
print("\n=== 图片加载测试 ===")
lprnet_dir = "LPRNET_part"
if os.path.exists(lprnet_dir):
for file in os.listdir(lprnet_dir):
if file.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp')):
image_path = os.path.join(lprnet_dir, file)
print(f"\n测试文件: {file}")
# 方法1: 普通cv2.imread
img1 = cv2.imread(image_path)
print(f"cv2.imread结果: {img1 is not None}")
# 方法2: 支持中文路径的方式
try:
img2 = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_COLOR)
print(f"cv2.imdecode结果: {img2 is not None}")
if img2 is not None:
print(f"图片尺寸: {img2.shape}")
except Exception as e:
print(f"cv2.imdecode失败: {e}")
if __name__ == "__main__":
# 首先测试图片加载
test_image_loading()
# 然后测试完整的识别流程
test_real_images()